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Journal articles on the topic 'Smart city, air quality, low-cost sensors'
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1
Morris, E., X. Liu, A. Manwar, D. Y. Zang, G. Evans, J. Brook, B. Rousseau, C. Clark, and J. MacIsaac. "APPLICATION OF DISTRIBUTED URBAN SENSOR NETWORKS FOR ACTIONABLE AIR QUALITY DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-4/W2-2020 (September 15, 2020): 119–26. http://dx.doi.org/10.5194/isprs-annals-vi-4-w2-2020-119-2020.
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Abstract. Ambient air pollution continues to be a major human health burden around the world. Cities with existing smart data infrastructure, and those with smart city aspirations, would benefit from the integration of real-time data from an air quality sensor network. AirSENCE™ is one such sensor which monitors eight common pollutants at low cost. It has been deployed in Canada in cities of the Greater Toronto Area, e.g. the City of Oshawa, to augment the existing urban data network and study the impacts of traffic flow and land usage on air quality. Results reveal that distributed sensors are highly useful for detecting localized pollution events that would otherwise go undetected, providing policymakers with a valuable, actionable data for protecting public health. Coupling air quality sensors with other smart city data (traffic monitors in this case) was shown to provide a more comprehensive representation of how air pollutant levels are affected by human activity, which can better inform city planning decisions.
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Stavroulas, Iasonas, Georgios Grivas, Panagiotis Michalopoulos, Eleni Liakakou, Aikaterini Bougiatioti, Panayiotis Kalkavouras, Kyriaki Fameli, Nikolaos Hatzianastassiou, Nikolaos Mihalopoulos, and Evangelos Gerasopoulos. "Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece." Atmosphere 11, no. 9 (August 29, 2020): 926. http://dx.doi.org/10.3390/atmos11090926.
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Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.
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Penza, Michele, Domenico Suriano, Valerio Pfister, Mario Prato, and Gennaro Cassano. "Wireless Sensors Network Monitoring of Saharan Dust Events in Bari, Italy." Proceedings 2, no. 13 (December 5, 2018): 898. http://dx.doi.org/10.3390/proceedings2130898.
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A sensors network based on 8 stationary nodes distributed in Bari (Southern Italy) hasbeen deployed for urban air quality monitoring during advection events of Saharan dust in theperiod 2015–2017. The low-cost sensor-systems have been installed in specific sites (buildings,offices, schools, streets, airport) to assess the PM10 concentration at high spatial and temporalresolution in order to supplement the expensive official air monitoring stations for citizen sciencepurposes. Continuous measurements were performed by a cost-effective optical particle counter(PM10), including temperature and relative humidity sensors. They are operated to assess theperformance during a long-term campaign (July 2015–December 2017) of 30 months for smart citiesapplications. The sensor data quality has been evaluated by comparison to the reference data of the9 Air Quality Monitoring Stations (AQMS), managed by local environmental agency (ARPA-Puglia)in the Bari city.
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Chapman, Lee, Catherine L. Muller, Duick T. Young, Elliott L. Warren, C. S. B. Grimmond, Xiao-Ming Cai, and Emma J. S. Ferranti. "The Birmingham Urban Climate Laboratory: An Open Meteorological Test Bed and Challenges of the Smart City." Bulletin of the American Meteorological Society 96, no. 9 (September 1, 2015): 1545–60. http://dx.doi.org/10.1175/bams-d-13-00193.1.
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Abstract The Birmingham Urban Climate Laboratory (BUCL) is a near-real-time, high-resolution urban meteorological network (UMN) of automatic weather stations and inexpensive, nonstandard air temperature sensors. The network has recently been implemented with an initial focus on monitoring urban heat, infrastructure, and health applications. A number of UMNs exist worldwide; however, BUCL is novel in its density, the low-cost nature of the sensors, and the use of proprietary Wi-Fi networks. This paper provides an overview of the logistical aspects of implementing a UMN test bed at such a density, including selecting appropriate urban sites; testing and calibrating low-cost, nonstandard equipment; implementing strict quality-assurance/quality-control mechanisms (including metadata); and utilizing preexisting Wi-Fi networks to transmit data. Also included are visualizations of data collected by the network, including data from the July 2013 U.K. heatwave as well as highlighting potential applications. The paper is an open invitation to use the facility as a test bed for evaluating models and/or other nonstandard observation techniques such as those generated via crowdsourcing techniques.
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Twahirwa, Evariste, James Rwigema, and Raja Datta. "Design and Deployment of Vehicular Internet of Things for Smart City Applications." Sustainability 14, no. 1 (December 24, 2021): 176. http://dx.doi.org/10.3390/su14010176.
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A novel computing paradigm, called the Internet of things emerged a few years ago. IoT is materialized by connecting both real and digital worlds together. The deployment of IoT in vehicular networks has introduced a new vehicular communication technology-themed vehicular internet of things (V-IoT). With the introduction of miniaturized sensors and actuators, V-IoT has demonstrated the ability to improve the level of urban transport systems through the development and deployment of low-cost but powerful technologies which seamlessly upgrade the level of smart transportation in urban environments. In this research article, we have presented the features of V-IoT that encompass both the benefits and potential challenges of the technology. Low-cost IoT prototypes have been built and tested for numerous functions in vehicular environments. The monitored parameters include air, road conditions such as traffics flow sizes, air quality, weather parameters, and signal status in terms of Received signal strength indicator, and Signal noise ratio for both road and intra-vehicular environments. Devices are implemented at every IoT architectural layer and tested on a web-based IoT front-end application using different protocols like LoRaWAN. Two LoRa sensors have been deployed in the public bus to monitor some of the mentioned parameters on a real-time basis and historical data could be retrieved through the developed web-based dashboard. Simplistic algorithms are implemented for both real-time and historical data demonstration.
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Shakhov, Vladimir, Andrei Materukhin, Olga Sokolova, and Insoo Koo. "Optimizing Urban Air Pollution Detection Systems." Sensors 22, no. 13 (June 24, 2022): 4767. http://dx.doi.org/10.3390/s22134767.
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Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.
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Marek, L., M. Campbell, M. Epton, M. Storer, and S. Kingham. "REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 9, 2016): 729–33. http://dx.doi.org/10.5194/isprs-archives-xli-b2-729-2016.
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The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.
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Marek, L., M. Campbell, M. Epton, M. Storer, and S. Kingham. "REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 9, 2016): 729–33. http://dx.doi.org/10.5194/isprsarchives-xli-b2-729-2016.
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The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.
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Zaripova, Viktoriia, Irirna Petrova, and Yuliya Lezhnina. "Biosensors application for the life systems quality in a smart city." E3S Web of Conferences 135 (2019): 03006. http://dx.doi.org/10.1051/e3sconf/201913503006.
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The article shows that the smart city management systems requires continuous monitoring of environmental parameters (air, soil and water quality, temperature, humidity, toxic impurities, etc.) and the placement of a large number of different sensors, among which biosensors occupy a leading position. The study analyzed patent documents for leading countries of the world (Great Britain, France, Japan, Germany, USA, Russia) and it showed a steady increase in the number of patents on biosensors. The reasons for the growth and continuous expansion of the scope of biosensors are: cost reduction, increased durability, speed, reliability, accuracy and sensitivity; increase in their functionality. The article presents a classification of biosensors indicating the percentage of patents by type of transducer and the number of patents by bioselective elements, as well as a description of an automated system for the synthesis of new biosensor designs based on the energy-information method. Paper is given the resulting solution can be used as a skeletal structure to create new biosensors, or improved using the knowledge base on techniques for improving the technical and operational characteristics of physical and technical effects.
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Lal, Raj M., Kirti Das, Yingling Fan, Karoline K. Barkjohn, Nisha Botchwey, Anu Ramaswami, and Armistead G. Russell. "Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City." Environmental Health Insights 14 (January 2020): 117863022091548. http://dx.doi.org/10.1177/1178630220915488.
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Cities in the United States have announced initiatives to become more sustainable, healthy, resilient, livable, and environmentally friendly. However, indicators for measuring all outcomes related to these targets and the synergies between them have not been well defined or studied. One such relationship is the linkage between air quality with emotional well-being (EWB) and neighborhood infrastructure. Here, regulatory monitoring, low-cost sensors (LCSs), and air quality modeling were combined to assess exposures to PM2.5 and traffic-related NOx in 6 Minneapolis, MN, neighborhoods of varying infrastructure parameters (median household income, urban vs suburban, and access to light rail). Residents of the study neighborhoods concurrently took real-time EWB assessments using a smart phone application, Daynamica, to gauge happiness, tiredness, stress, sadness, and pain. Both LCS PM2.5 observations and mobile-source-simulated NOx were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM2.5 differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NOx was statistically significantly (α = 0.05) higher in the 4 urban neighborhoods than in the 2 suburban neighborhoods. Close proximity to light rail had no observable impact on average observed PM2.5 or simulated mobile-source NOx. Home-based exposure assessments found that PM2.5 was negatively correlated with positive emotions such as happiness and to net affect (the sum of positive and negative emotion scores) and positively correlated (ie, a higher PM2.5 concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NOx, assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NOx levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NOx impacts. Although none of the air quality and EWB responses were determined to be statistically significant (α = 0.05), due in part to the relatively small sample size, the results are suggestive of linkages between air quality and a variety of EWB outcomes.
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O’Keeffe, Kevin P., Amin Anjomshoaa, Steven H. Strogatz, Paolo Santi, and Carlo Ratti. "Quantifying the sensing power of vehicle fleets." Proceedings of the National Academy of Sciences 116, no. 26 (June 11, 2019): 12752–57. http://dx.doi.org/10.1073/pnas.1821667116.
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Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city’s health. Recently, a “drive-by” paradigm has been proposed in which sensors are deployed on third-party vehicles, enabling wide coverage at low cost. Research on drive-by sensing has mostly focused on sensor engineering, but a key question remains unexplored: How many vehicles would be required to adequately scan a city? Here, we address this question by analyzing the sensing power of a taxi fleet. Taxis, being numerous in cities, are natural hosts for the sensors. Using a ball-in-bin model in tandem with a simple model of taxi movements, we analytically determine the fraction of a city’s street network sensed by a fleet of taxis during a day. Our results agree with taxi data obtained from nine major cities and reveal that a remarkably small number of taxis can scan a large number of streets. This finding appears to be universal, indicating its applicability to cities beyond those analyzed here. Moreover, because taxis’ motion combines randomness and regularity (passengers’ destinations being random, but the routes to them being deterministic), the spreading properties of taxi fleets are unusual; in stark contrast to random walks, the stationary densities of our taxi model obey Zipf’s law, consistent with empirical taxi data. Our results have direct utility for town councilors, smart-city designers, and other urban decision makers.
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Středová, Hana, Tomáš Středa, and Tomáš Litschmann. "Smart tools of urban climate evaluation for smart spatial planning." Moravian Geographical Reports 23, no. 3 (September 1, 2015): 47–57. http://dx.doi.org/10.1515/mgr-2015-0017.
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Abstract Air temperature and humidity conditions were monitored in Hradec Králové, Czech Republic, by a network of meteorological stations. Meteorological sensors were placed across a representative variety of urban and suburban environments. The data collected over the 2011–2014 period are analysed in this paper. The data from reference standard meteorological stations were used for comparison and modelling purposes. Air temperatures at the points of interest were successfully modelled using regression relationships. The spatial expression of point measurements of air temperatures was provided by GIS methods in combination with CORINE land cover layer, and satellite thermal images were used to evaluate the significance of these methods. The use of standard climate information has low priority for urban planners. The impact of the urban heat island on city residents and visitors was evaluated using the HUMIDEX index, as it is more understandable for urban planners than temperature conditions as such. The aim of this paper is the modification, description and presentation of urban climate evaluation methods that are easily useable for spatial planning purposes. These methods are based on comprehensible, easily available but quality data and results. This unified methodology forms a theoretical basis for better urban planning policies to mitigate the urban heat island effects.
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Zou, Xiangyu, Jinjin Zhao, Duan Zhao, Bin Sun, Yongxin He, and Stelios Fuentes. "Air Quality Prediction Based on a Spatiotemporal Attention Mechanism." Mobile Information Systems 2021 (February 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/6630944.
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With the rapid development of the Internet of Things and Big Data, smart cities have received increasing attention. Predicting air quality accurately and efficiently is an important part of building a smart city. However, air quality prediction is very challenging because it is affected by many complex factors, such as dynamic spatial correlation between air quality detection sensors, dynamic temporal correlation, and external factors (such as road networks and points of interest). Therefore, this paper proposes a long short-term memory (LSTM) air quality prediction model based on a spatiotemporal attention mechanism (STA-LSTM). The model uses an encoder-decoder structure to model spatiotemporal features. A spatial attention mechanism is introduced in the encoder to capture the relative influence of surrounding sites on the prediction area. A temporal attention mechanism is introduced in the decoder to capture the time dependence of air quality. In addition, for spatial data such as point of interest (POI) and road networks, this paper uses the LINE graph embedding method to obtain a low-dimensional vector representation of spatial data to obtain abundant spatial features. This paper evaluates STA-LSTM on the Beijing dataset, and the root mean square error (RMSE) and R-squared ( R 2 ) indicators are used to compare with six benchmarks. The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.
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Alpan, K., and B. Sekeroglu. "PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 21–27. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-21-2020.
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Abstract. Air pollution, which is one of the biggest problems created by the developing world, reaches severe levels, especially in urban areas. Weather stations established at certain points in countries regularly obtain data and inform people about air quality. In Smart City applications, it is aimed to perform this process with higher speed and accuracy by collecting data with thousands of sensors based on the Internet of Things. At this stage, artificial intelligence and machine learning plays a vital role in analyzing the data to be obtained. In this study, six pollutant concentrations; particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), Ozone (O3), and carbon monoxide (CO), were predicted using three basic machine learning algorithms, namely, random forest, decision tree and support vector regression, by considering only meteorological data. Experiments on two different datasets showed that the random forest has a high prediction capacity (R2: 0.74–0.86), and high-accuracy predictions can be performed on pollutant concentrations using only meteorological data. This and further studies based on meteorological data would help to reduce the number of devices in Smart City applications and will make it more cost-effective.
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Banach, Marzena, Rafał Długosz, Tomasz Talaśka, and Witold Pedrycz. "Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme." Remote Sensing 14, no. 2 (January 17, 2022): 413. http://dx.doi.org/10.3390/rs14020413.
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The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed.
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Kapoor, Nishant Raj, Ashok Kumar, Anuj Kumar, Aman Kumar, Mazin Abed Mohammed, Krishna Kumar, Seifedine Kadry, and Sangsoon Lim. "Machine Learning-Based CO2 Prediction for Office Room: A Pilot Study." Wireless Communications and Mobile Computing 2022 (March 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/9404807.
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Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. The testing and monitoring of CO2 consume cost and time and require smart sensors. Thus, to solve these limitations, machine learning (ML) has been used to predict the concentration of CO2 inside an office room. This study is based on the data collected through real-time measurements of indoor CO2, number of occupants, area per person, outdoor temperature, outer wind speed, relative humidity, and air quality index used as input parameters. In this study, ten algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), Gaussian process regression (GPR), linear regression (LR), ensemble learning (EL), optimized GPR, optimized EL, optimized DT, and optimized SVM, were used to predict the concentration of CO2. It has been found that the optimized GPR model performs better than other selected models in terms of prediction accuracy. The result of this study indicated that the optimized GPR model can predict the concentration of CO2 with the highest prediction accuracy having R , RMSE, MAE, NS, and a20-index values of 0.98874, 4.20068 ppm, 3.35098 ppm, 0.9817, and 1, respectively. This study can be utilized by the designers, researchers, healthcare professionals, and smart city developers to analyse the indoor air quality for designing air ventilation systems and monitoring CO2 level inside the buildings.
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SEVASTYANOV, RUSLAN. "ACTUAL PROBLEMS OF DEVELOPMENT OF SMART CITIES." Herald of Khmelnytskyi National University 292, no. 2 (May 2021): 170–75. http://dx.doi.org/10.31891/2307-5740-2021-292-2-28.
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The article is devoted to the development of the “smart city”. This topic is important to ensure the effective functioning of modern cities and take into account the needs of their inhabitants. Smart City is an innovative system. This system uses sensors, internet of things (IoT), networks, big data to increase the competitiveness and efficiency of urban services. It is established that the sustainable development of a “smart city” must take into account economic, social and environmental aspects and ensure its compliance with the needs of present and future generations. The development of “smart cities” involves the integration and coordination of services in cities. Such development should provide opportunities for remote participation of citizens in city management. Strategic directions of Zaporizhzhya city development are considered. The development strategy of Zaporizhzhya until 2028 provides for the development of a healthy environment, resource conservation, promotion of entrepreneurship, creative economy, improving the quality of life. In Zaporizhzhya, the main goals of the development of a “smart city” are to increase the share of waste processing, reduce air pollution in the city, reduce water use by industrial enterprises. The sphere of entrepreneurship and creative economy envisages the development of processing industries and logistics with low technogenic impact on the environment, reduction of emissions and introduction of ecological technologies. For the development of the creative economy of Zaporizhzhya, gradual commercialization of science, holding of art projects and festivals, increase of coworking and popularization of IT-technologies are envisaged. The article systematizes electronic smart services in Zaporizhzhya. In particular, the city is developing electronic smart services to improve the further comfort of citizens (“EasyWay”, “Waze” and others). The development of smart city services in Zaporizhzhya covers the areas of transport, housing and communal services, health and medicine, cultural and entertainment, socio-economic and administrative-social services. Such actions help to accelerate management decisions, save budget funds, improve the provision of information services for citizens and businesses.
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Alsina-Pagès, Rosa, and Pau Bergadà. "The Citizen as a Key Point of the Policies: A First Approach to Auralization for the Acoustic Perception of Noise in an Urban Environment." Proceedings 4, no. 1 (November 14, 2018): 11. http://dx.doi.org/10.3390/ecsa-5-05720.
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The improvement of the quality of life in the framework of the smart city paradigm cannot be limited to a set of objective measures carried out over several critical parameters (e.g., noise, air pollution). The citizen’s perception of the problem to be solved, as well as the perception of the improvement achieved with the policies defined for this purpose are more important than the objectivity and the measurement of the change achieved. A first auralization approach for the evaluation of the acoustic perception of street noise is presented in this work. The wireless acoustic sensor network can pick up street noise and can even record specific sounds that reach a higher equivalent level for study, but the most important thing for administration is whether the neighbor has noticed an improvement in the quality of life. This work is a first approximation to an estimation of the real perception of citizens of the street urban noises collected by a low-cost wireless acoustic sensor network.
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Abdul Jabbar, A., I. Aicardi, N. Grasso, and M. Piras. "URBAN DATA COLLECTION USING A BIKE MOBILE SYSTEM WITH A FOSS ARCHITECTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 3–9. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-3-2017.
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European community is working to improve the quality of the life in each European country, in particular to increase the quality air condition and safety in each city. The quality air is daily monitored, using several ground station, which do not consider the variation of the quality during the day, evaluating only the average level. In this case, it could be interesting to have a “smart” system to acquire distributed data in continuous, even involving the citizens. On the other hand, to improve the safety level in urban area along cycle lane, road and pedestrian path, exist a lot of algorithms for visibility and safety analysis; the crucial aspect is the 3D model considered as “input” in these algorithms, which always needs to be updated. <br><br> A bike has been instrumented with two digital camera as Raspberry PI-cam. Image acquisition has been realized with a dedicated python tool, which has been implemented in the Raspberry PI system. Images have been georeferenced using a u-blox 8T, connected to Raspberry system. GNSS data has been acquired using a specific tool developed in Python, which was based on RTKLIB library. Time synchronization has been obtained with GNSS receiver. Additionally, a portable laser scanner, an air quality system and a small Inertial platform have been installed and connected with the Raspberry system. <br><br> The system has been implemented and tested to acquire data (image and air quality parameter) in a district in Turin. Also a 3D model of the investigated site has been carried. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be described. different low cost sensors, in particular digital camera and laser scanner to collect easily geospatial data in urban area.
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Zhang, Yanxu, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, et al. "Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors." Atmospheric Chemistry and Physics 21, no. 4 (February 26, 2021): 2917–29. http://dx.doi.org/10.5194/acp-21-2917-2021.
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Abstract. Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution > 1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg m−3) agree well with the measurements (0.90 ± 0.58 mg m−3, n=114 502). The model has similar spatial patterns to those of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼ 20∘. We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼ 50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.
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Siouti, Evangelia, Ksakousti Skyllakou, Ioannis Kioutsioukis, David Patoulias, George Fouskas, and Spyros Pandis. "Development and Application of the SmartAQ High-Resolution Air Quality and Source Apportionment Forecasting System for European Urban Areas." Atmosphere 13, no. 10 (October 16, 2022): 1693. http://dx.doi.org/10.3390/atmos13101693.
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Air pollution forecasting systems are useful tools for the reduction in human health risks and the eventual improvement of atmospheric quality on regional or urban scales. The SmartAQ (Smart Air Quality) forecasting system combines state-of-the-art meteorological and chemical transport models to provide detailed air pollutant concentration predictions at a resolution of 1 × 1 km2 for the urban area of interest for the next few days. The Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model is used to produce meteorological fields and the PMCAMx (Particulate Matter Comprehensive Air quality Model with extensions) chemical transport model for the simulation of air pollution. SmartAQ operates automatically in real time and provides, in its current configuration, a three-day forecast of the concentration of tens of gas-phase air pollutants (NOx, SO2, CO, O3, volatile organic compounds, etc.), the complete aerosol size/composition distribution, and the source contributions for all primary and secondary pollutants. The system simulates the regional air quality in Europe at medium spatial resolution and can focus, using high resolution, on any urban area of the continent. The city of Patras in Greece is used for the first SmartAQ application, taking advantage of the available Patras’ dense low-cost sensor network for PM2.5 (particles smaller than 2.5 μm) concentration measurements. Advantages of SmartAQ include (a) a high horizontal spatial resolution of 1 × 1 km2 for the simulated urban area; (b) advanced treatment of the organic aerosol volatility and chemistry; (c) use of an updated emission inventory that includes not only the traditional sources (industry, transport, agriculture, etc.), but also biomass burning from domestic heating and cooking; (d) forecasting of not only the pollutant concentrations, but also of the sources contributions for each one of them using the Particulate matter Source Apportionment Technology (PSAT) algorithm.
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Omidvarborna, Hamid, Prashant Kumar, Joe Hayward, Manik Gupta, and Erick Giovani Sperandio Nascimento. "Low-Cost Air Quality Sensing towards Smart Homes." Atmosphere 12, no. 4 (April 2, 2021): 453. http://dx.doi.org/10.3390/atmos12040453.
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The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.
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Karadag, Ilker, and Aysem Berrin Cakmakli. "Interface of the Natural Ventilation Systems with Building Management Systems." Periodica Polytechnica Architecture 51, no. 2 (November 27, 2020): 178–88. http://dx.doi.org/10.3311/ppar.15700.
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The vertical city is increasingly being seen as the most viable solution for many urban centers. However, being vertical means constructing tall buildings which imply a large amount of energy requirement mostly due to the mechanical ventilation systems. Replacing these systems with natural ventilation is of importance on the way of achieving sustainable buildings and cities. However, there are many challenges in incorporating natural ventilation systems into tall office buildings. Because, having a far distance from the ground levels, tall buildings are exposed to turbulent and unstable wind conditions. Therefore, advanced technical systems to monitor/control a tall building is required even though natural ventilation is mostly considered a passive, low technological approach to condition a building. These systems utilize sensors to measure internal environmental conditions and external conditions of air temperature, humidity, wind velocity, and rain to utilize passive or active modes, based on the data received. To integrate such systems, in most cases, a hybrid approach is required to fill the gap between the natural and mechanical ventilation systems. Since, climate conditions may not be suitable for solely depending on natural ventilation throughout the year, using mechanical ventilation systems as a back-up to natural ventilation is mostly required. Based on this approach, this study presents advanced natural ventilation strategies of a number of buildings integrating Building Management Systems. Throughout the study, it is intended to guide further researches on natural ventilation and consequently to contribute to the environmental quality of urban areas and smart sustainable development of the cities.
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Johnston, Steven J., Philip J. Basford, Florentin M. J. Bulot, Mihaela Apetroaie-Cristea, Natasha H. C. Easton, Charlie Davenport, Gavin L. Foster, Matthew Loxham, Andrew K. R. Morris, and Simon J. Cox. "City Scale Particulate Matter Monitoring Using LoRaWAN Based Air Quality IoT Devices." Sensors 19, no. 1 (January 8, 2019): 209. http://dx.doi.org/10.3390/s19010209.
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Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment, six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that (i) the physical device developed can operate at a city scale; (ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends; (iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project, a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.
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Lehmann, K., A. Minhans, M. K. Fajari, and M. Hahn. "ASSESSMENT OF LOW-COST PARTICULATE MATTER SENSORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 671–77. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-671-2019.
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Abstract. The effect of particulate matter is increasingly gaining significance due to its harmful effects on human and urban ecosystems. In view of it, many communities worldwide are collecting air quality data privately to influence their policy makers to make stricter provisions for reducing harmful emissions and thereby improving their quality of life. Likewise, in many German cities, a community of air quality monitors which rely on low-cost PM Sensors is gathering momentum. Such communities possess privately-owned & low-cost air quality monitoring devices that claim to accurately measure PM concentrations and are openly accessible via internet. One such initiative is an air quality monitoring network viz. “luftdaten.info”, which contains of more than 300 low-cost sensors that consistently obtains PM data, colloquially referred as fine dust, in the city of Stuttgart as well as its surrounding districts. Besides, eight stations are continuously monitoring PM concentration in Stuttgart; these are operated by the State Environmental Agency (LuBW- Landesanstalt für Umwelt Baden-Württemberg). Stuttgart University of Applied Sciences (HFT) has currently installed 7 low-cost PM sensors to monitor and study PM concentration in one of its projects. This study endeavors to relate PM 2.5 and PM 10.0 using low-cost sensors. It intends to investigate the reliability of the measured PM concentration using such low-costs sensors once these are placed horizontally and vertically apart and comparing the measures of the 7 sensors. Another objective is to compare the PM concentration measurements with a meteorological station operated by the State of Baden-Wuerttemberg in the vicinity. A correlation analysis is performed to develop understanding of relationships of PM concentration with meteorological parameters, viz. with respect to ambient temperature, air pressure, humidity, wind speed and wind direction. Furthermore, it attempts to develop a regression model using above listed meteorological parameters. Finally, deficiencies in the measurement of low-costs and its placement effects are commented. Further suggestions are made for improving the data capturing and analytical procedures while using low-cost sensors.
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Lezina, Elena, and Mikhail Misyurev. "Ambient air quality measurements in a large city: existing solutions, new opportunities and challenges." Journal of Physics: Conference Series 2192, no. 1 (March 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2192/1/012031.
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Abstract The city of Moscow has been actively introducing advanced technologies in the field of pollution control in real time. At the same time, Russian legislation often lags behind modern trends. The article discusses the issues of introducing reference and equivalent measurement methods and the problem of introducing small-sized instruments (low-cost sensors) for monitoring atmospheric air pollution. The article presents preliminary results of large-scale tests of low-cost sensors in comparison with traditional measurement methods, which were carried out by the State Environmental Protection Institution "Mosecomonitoring".
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Alvear-Puertas, Vanessa E., Yadira A. Burbano-Prado, Paul D. Rosero-Montalvo, Pınar Tözün, Fabricio Marcillo, and Wilmar Hernandez. "Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador." Sensors 22, no. 18 (September 16, 2022): 7015. http://dx.doi.org/10.3390/s22187015.
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Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.
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Cieplak, Tomasz, Tomasz Rymarczyk, and Robert Tomaszewski. "A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers." MATEC Web of Conferences 252 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201925203009.
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This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.
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Arco, E., P. Boccardo, F. Gandino, A. Lingua, F. Noardo, and M. Rebaudengo. "AN INTEGRATED APPROACH FOR POLLUTION MONITORING: SMART ACQUIREMENT AND SMART INFORMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4/W1 (August 25, 2016): 67–74. http://dx.doi.org/10.5194/isprs-annals-iii-4-w1-67-2016.
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Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use.
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Arco, E., P. Boccardo, F. Gandino, A. Lingua, F. Noardo, and M. Rebaudengo. "AN INTEGRATED APPROACH FOR POLLUTION MONITORING: SMART ACQUIREMENT AND SMART INFORMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W1 (September 5, 2016): 67–74. http://dx.doi.org/10.5194/isprs-annals-iv-4-w1-67-2016.
Full textAbstract:
Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use.
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Sales-Lérida, Diego, Alfonso J. Bello, Alberto Sánchez-Alzola, and Pedro Manuel Martínez-Jiménez. "An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis." Sensors 21, no. 14 (July 13, 2021): 4781. http://dx.doi.org/10.3390/s21144781.
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Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.
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Mueller, Michael, Jonas Meyer, and Christoph Hueglin. "Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich." Atmospheric Measurement Techniques 10, no. 10 (October 17, 2017): 3783–99. http://dx.doi.org/10.5194/amt-10-3783-2017.
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Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models also include time-dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high-quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.
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Kumar, Arvaan, and Sriroop Chaudhuri. "Improving Urban Air Quality Monitoring in Delhi, India: Reflections on Low-Cost Air Quality Sensors (LCAQS) and Participatory Engagement." Environment and Urbanization ASIA 13, no. 2 (September 2022): 265–83. http://dx.doi.org/10.1177/09754253221122752.
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In Delhi, the capital city of India, air pollution has been a perpetual menace to urban sustainability and public health. The present study uses a mixed-method approach to enumerate to the urban authorities: (a) the state of air pollution in the city; (b) systemic flaws in the current monitoring network; (c) potential means to bolster it; and (d) need of a participatory framework for monitoring. Information about Air Quality Index (AQI), obtained from 36 monitoring stations across Delhi is compared between 2021 (20 April–25 May; 2nd year/phase of SARS-CoV-2 lockdown), and the corresponding time periods in 2020 (1st year/phase of lockdown), and 2019 (business-as-usual) using the Mann–Whitney U Test. AQI during the 2021 lockdown (a) appeared statistically more similar ( p < .01) to that of 2019 and (b) exceeded the environmental health safety benchmark for 85% days during the study period (20 April–25 May). However, this only presented a partial glimpse into the air pollution status. It owes to numerous ‘holes’ in the AQI data record (no data and/or insufficient data). Moreover, certain areas in Delhi yet have no monitoring station, or only too few, to yield a ‘representative’ estimate (inadequate spatial coverage). Such shortcomings in the existing monitoring network may deter future research and targeted/informed decision-making for pollution control. To that end, the present research offers a summary view of Low-Cost Air Quality Sensors (LCAQS), to offer the urban sustainability authorities, ‘complementary’ technique to bolster and diversify the existing network. The main advantages and disadvantages of various LCAQS sensor technologies are highlighted while emphasizing on the challenges around various calibration techniques (linear and non-linear). The final section reflects on the integration of science and technology with social dimensions of air quality monitoring and highlights key requirements for (a) community mobilization and (b) stakeholder engagement to forge a participatory systems’ design for LCAQS deployment.
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Parés, M. E., D. Garcia, and F. Vázquez-Gallego. "MAPPING AIR QUALITY WITH A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM (C-AQM)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 685–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-685-2020.
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Abstract. World cities are currently facing one of the major crisis of the last century. Some preliminary studies on COVID-19 pandemia have shown that air pollutants may have a strong impact on virus effects. Improved gas sensors and wireless communication systems open the door to the design of new air monitoring systems based on citizen science to better monitor and communicate the air quality levels. In this paper, we present the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on Air Quality Monitoring reference stations and a cluster of new low-cost and low-energy sensor nodes, in order to improve the resolution of air quality maps. The data collected by the C-AQM system is stored in a time series database and is available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main bases of the C-AQM system as well as the measurements validation campaign carried out.
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Chen, Mingjian, Weichang Yuan, Chang Cao, Colby Buehler, Drew R. Gentner, and Xuhui Lee. "Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment." Sensors 22, no. 7 (April 4, 2022): 2767. http://dx.doi.org/10.3390/s22072767.
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The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist’s smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 μg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 μg m−3) and in two cities with high background concentrations (41 to 74 μg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.
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Lai, Xiaozheng, Ting Yang, Zetao Wang, and Peng Chen. "IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction." Applied Sciences 9, no. 9 (May 2, 2019): 1831. http://dx.doi.org/10.3390/app9091831.
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In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.
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Nguyen, Chi Doan Thien, and Hien Thi To. "Evaluating the applicability of a low-cost sensor for measuring PM2.5 concentration in Ho Chi Minh city, Viet Nam." Science and Technology Development Journal 22, no. 3 (October 7, 2019): 343–47. http://dx.doi.org/10.32508/stdj.v22i3.1688.
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Introduction: Continuous monitoring provides real-time data which is helpful for measuring air quality; however, these systems are often very expensive, especially for developing countries such as Vietnam. The use of low-cost sensors for monitoring air pollution is a new approach in Vietnam and this study assesses the utility of low-cost, light-scattering-based, particulate sensors for measuring PM2.5 concentrations in Ho Chi Minh City.
Methods: The low-cost sensors were compared with both a Beta attenuation monitor (BAM) reference method and a gravimetric method during the rainy season period of October to December 2018.
Results: The results showed that there was a very strong correlation between two low-cost sensors (R = 0.97, slope = 1.0), and that the sensor precision varied from 0 to 21.4% with a mean of 3.1%. Both one-minute averaged data and one-hour averaged data showed similar correlations between sensors and BAM (R2 = 0.62 and 0.69, respectively), while 24-hour averaged data showed excellent agreement (R2 = 0.95, slope = 1.05). In addition, we also found a strong correlation between those instruments and a gravimetric method using 24-hour averaged data. A linear regression was used to calibrate the 24-hour averaged sensor data and, once calibrated, the bias dropped to zero.
Conclusion: These results show that low-cost sensors can be used for daily measurements of PM2.5 concentrations in Ho Chi Minh City. The effect of air conditions, such as temperature and humidity, should be conducted. Moreover, technical methods to improve time resolution of lowcost sensors need to be developed and applied in order to provide real-time measurements at an inexpensive cost.
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Rahman, A., M. Usama, M. Tahir, and M. Uppal. "DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W5-2022 (October 17, 2022): 167–73. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w5-2022-167-2022.
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Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.
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Davidović, Miloš, Sonja Dmitrašinović, Maja Jovanović, Jelena Radonić, and Milena Jovašević-Stojanović. "Diurnal, Temporal and Spatial Variations of Main Air Pollutants Before and during Emergency Lockdown in the City of Novi Sad (Serbia)." Applied Sciences 11, no. 3 (January 28, 2021): 1212. http://dx.doi.org/10.3390/app11031212.
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Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO2 levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO2, NO, and NOX during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors’ usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.
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González Rivero, Rosa Amalia, Luis Ernesto Morera Hernández, Olivier Schalm, Erik Hernández Rodríguez, Daniellys Alejo Sánchez, Mayra C. Morales Pérez, Vladimir Nuñez Caraballo, Werner Jacobs, and Alain Martinez Laguardia. "A Low-Cost Calibration Method for Temperature, Relative Humidity, and Carbon Dioxide Sensors Used in Air Quality Monitoring Systems." Atmosphere 14, no. 2 (January 17, 2023): 191. http://dx.doi.org/10.3390/atmos14020191.
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Low-cost sensors provide an affordable alternative to monitor environmental parameters with acceptable performance. There is a substantial amount of literature where low-cost sensors are compared with high-end reference measurements. However, not all organizations have access to such reference infrastructure. We propose low-cost calibration methods for temperature, relative humidity, and CO2 to allow them to collect their own reliable data. These methods are based on simple techniques and procedures that allow temperature calibration to be achieved in the range of 0 to 50 °C, relative humidity from 0 to 90%, and CO2 between 0 and 1100 ppm. The materials used to create the calibration setups can be purchased online, at hardware stores, and in pharmacies. The reliability of the calibration methods was evaluated using several indicators, such as the airtightness of the calibration box, similarity with the factory calibration, similarity with the reference, similarity between different sensors, replicability of the calibration method, and the similarity with a golden standard. In addition, the results of the low-cost calibration methods were compared with the more advanced calibration methods. A short measurement campaign in the city of Santa Clara, Cuba, demonstrated that such calibrations transform in-house developed monitoring systems into valid low-cost scientific instruments for decision-making. This work creates opportunities for institutions and researchers hosted in low- and mid-income countries to build and validate their own equipment to reliably solve local problems.
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Budde, Matthias, Simon Leiner, Marcel Köpke, Johannes Riesterer, Till Riedel, and Michael Beigl. "FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor." Sensors 19, no. 3 (February 12, 2019): 749. http://dx.doi.org/10.3390/s19030749.
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Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.
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Starace, Giuseppe, Amber Tiwari, Gianpiero Colangelo, and Alessandro Massaro. "Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach." Electronics 11, no. 23 (November 25, 2022): 3904. http://dx.doi.org/10.3390/electronics11233904.
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This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.
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Pope, Francis D., Michael Gatari, David Ng'ang'a, Alexander Poynter, and Rhiannon Blake. "Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors." Atmospheric Chemistry and Physics 18, no. 20 (October 26, 2018): 15403–18. http://dx.doi.org/10.5194/acp-18-15403-2018.
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Abstract. East African countries face an increasing threat from poor air quality stemming from rapid urbanization, population growth, and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low-cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an AlphaSense OPC-N2 ran with a Raspberry Pi low-cost microcomputer, packaged in a weather-proof box. Measurements were conducted over a 2-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting 2 weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8±2.0 % in the fine particle fraction (PM2.5). The low-cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi.The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1 and 8.8 µg m−3, respectively. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8 and 13.0 µg m−3, respectively. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0 and 19.5 µg m−3, respectively. The urban measurements in Nairobi showed that PM concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow-type approach we can estimate the urban and roadside increments that are applicable to Nairobi (Lenschow et al., 2001). The median urban increment is 33.1 µg m−3 and the median roadside increment is 43.3 µg m−3 for PM2.5. For PM1, the median urban increment is 4.7 µg m−3 and the median roadside increment is 12.6 µg m−3. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi.A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high roadside increment indicating that vehicular traffic is a dominant source of PM in the city, accounting for approximately 48.1 %, 47.5 % and 57.2 % of the total PM loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low-cost sensors for PM measurement without gravimetric calibration available at all sites are discussed.This study shows that calibrated low-cost sensors can be successfully used to measure air pollution in cities like Nairobi. It demonstrates that low-cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.
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Chodorek, Agnieszka, Robert Ryszard Chodorek, and Paweł Sitek. "Response Time and Intrinsic Information Quality as Criteria for the Selection of Low-Cost Sensors for Use in Mobile Weather Stations." Electronics 11, no. 15 (August 7, 2022): 2448. http://dx.doi.org/10.3390/electronics11152448.
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Smart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, i.e., the two-stage selection of sensors, carried out on the basis of both the literature (pre-selection) and experiments (actual selection). We formulated the criteria of the sensor selection for the needs of the sources of weather information: the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming (due to the major criterion) and the most accurate (due to the minor criteria) set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.
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Telagam, Nagarjuna, Nehru Kandasamy, Nagendra Prasad G, and Menakadevi Nanjundan. "Smart Sensor Network Based High Quality Air Pollution Monitoring System Using Labview." International Journal of Online Engineering (iJOE) 13, no. 08 (August 4, 2017): 79. http://dx.doi.org/10.3991/ijoe.v13i08.7161.
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A ZigBee based wireless sensor network is implemented in this paper which is of low-cost solar-powered air quality monitoring system. The main objective of the proposed architecture is to interfacing various sensors to measure the sensor analog data and displayed in LabVIEW on the monitor using the graphical user interface (GUI). The real time ambient air quality monitoring in smart cities is of greater significance for the health of people. The wireless network sensor nodes are placed at different traffic signals in the smart cities which collect and report real-time data on different gases which are present in the environment such as carbon monoxide (CO), nitrogen dioxide (NO2), methane (CH4) and humidity. The proposed system allows smart cities to monitor air quality conditions on a desktop/laptop computer through an application designed using graphical programming based LabVIEW software and provides an alert if the air quality characteristics exceed acceptable levels. The sensor network was successfully tested on the campus of the institute of aeronautical engineering, Hyderabad. The sensor data are indicated by different indicators on the front panel of LabVIEW and also different charts are plotted with respect to time and amplitude which explains the severity of polluted areas.
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Apostolopoulos, Ioannis D., George Fouskas, and Spyros N. Pandis. "Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models." Atmosphere 14, no. 2 (February 13, 2023): 368. http://dx.doi.org/10.3390/atmos14020368.
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Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R2 improved from 0.22 to 0.86. Similar results were obtained for O3, wherein the mean error was reduced from 13 to 4.3 ppb and R2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants.
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Kimbrough, Sue, Stephen Krabbe, Richard Baldauf, Timothy Barzyk, Matthew Brown, Steven Brown, Carry Croghan, et al. "The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project." Chemosensors 7, no. 2 (May 27, 2019): 26. http://dx.doi.org/10.3390/chemosensors7020026.
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Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 µm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 µg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 µg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.
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Parés, M. E., and F. Vázquez-Gallego. "C-AQM: A CROWD-SOURCED AIR QUALITY MONITORING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 491–97. http://dx.doi.org/10.5194/isprs-archives-xlii-4-491-2018.
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<p><strong>Abstract.</strong> European cities are currently facing one of the main evolutions of the last fifty years. “Cities for the citizens” is the new leitmotiv of modern societies, and citizens are demanding, among others, a greener environment including non-polluted air. Improved sensors and improved communication systems open the door to the design of new systems based on citizen science to better monitor the air quality. In this paper, we present a system that relies on the already available Copernicus Environment Service, on Air Quality Monitoring reference stations and on a cluster of new low-cost, low-energy sensor nodes that will improve the resolution of air quality maps. The data collected by this system will be stored in a time series database, and it will be available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main challenges imposed by Air Quality Monitoring systems, our proposal to overcome those challenges, and the results of our preliminary tests.</p>
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Haiahem, Rahim, Pascale Minet, Selma Boumerdassi, and Leila Azouz Saidane. "An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN." Journal of Sensor and Actuator Networks 9, no. 3 (September 9, 2020): 42. http://dx.doi.org/10.3390/jsan9030042.
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High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM.
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Gómez-Suárez, Jaime, Patricia Arroyo, Raimundo Alfonso, José Ignacio Suárez, Eduardo Pinilla-Gil, and Jesús Lozano. "A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists." Sensors 22, no. 3 (February 8, 2022): 1272. http://dx.doi.org/10.3390/s22031272.
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We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user’s understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.